如何在一个数组python中合并两个不同数组的值

Question

我有一个解决特定问题的方法

[[0.34 0.26 0.76 ]
 [0.79 0.82 0.37 ]
 [0.93 0.87 0.94]]

对于同样的问题，我有另一个解决方案

[[0.21 0.73 0.69 ]
 [0.35 0.24 0.53]
 [0.01 0.42 0.50]]

现在我必须将它们的第 i 个位置合并在一起，这样结果数组就会像

[[0.34 0.21]
[0.26 0.73]
[0.76 0.69]
[0.79 0.35]
..........
..........

Answer 1

设置

x = np.array([[0.34, 0.26, 0.76 ],  [0.79, 0.82, 0.37 ],  [0.93, 0.87, 0.94]])
y = np.array([[0.21, 0.73, 0.69 ],  [0.35, 0.24, 0.53],  [0.01, 0.42, 0.50]])

dstack 和 ravel

np.dstack([x.ravel(), y.ravel()])

array([[[0.34, 0.21],
        [0.26, 0.73],
        [0.76, 0.69],
        [0.79, 0.35],
        [0.82, 0.24],
        [0.37, 0.53],
        [0.93, 0.01],
        [0.87, 0.42],
        [0.94, 0.5 ]]])

如果您担心这个引入的额外维度，您可以vstack 并转置：

np.vstack([x.ravel(), y.ravel()]).T

array([[0.34, 0.21],
       [0.26, 0.73],
       [0.76, 0.69],
       [0.79, 0.35],
       [0.82, 0.24],
       [0.37, 0.53],
       [0.93, 0.01],
       [0.87, 0.42],
       [0.94, 0.5 ]])

使用 np.column_stack

的另一种选择

np.column_stack([x.ravel(), y.ravel()])

Answer 2

您可以在 2 个数组上使用 vstack 并适当地重塑：

np.vstack([arr1,arr2]).reshape(2,-1).T

例子：

>>> arr1
array([[ 0.34,  0.26,  0.76],
       [ 0.79,  0.82,  0.37],
       [ 0.93,  0.87,  0.94]])
>>> arr2
array([[ 0.21,  0.73,  0.69],
       [ 0.35,  0.24,  0.53],
       [ 0.01,  0.42,  0.5 ]])

>>> np.vstack([arr1,arr2]).reshape(2,-1).T
array([[ 0.34,  0.21],
       [ 0.26,  0.73],
       [ 0.76,  0.69],
       [ 0.79,  0.35],
       [ 0.82,  0.24],
       [ 0.37,  0.53],
       [ 0.93,  0.01],
       [ 0.87,  0.42],
       [ 0.94,  0.5 ]])

Answer 3

这是一个不需要 numpy 的单行：

[list(a) for a in zip(sum(x, []), sum(y, []))]

sum(x, []) 将列表列表扁平化为单个扁平列表。然后我们将两个列表压缩在一起并列出元素。

Answer 4

你可以这样使用ravel()和numpy.concatenate(x,y,axis)：

np.concatenate((np.reshape(x.ravel(),(-1,1)),np.reshape(y.ravel(),(-1,1))),axis=1)

[[ 0.34  0.21]
 [ 0.26  0.73]
 [ 0.76  0.69]
 [ 0.79  0.35]
 [ 0.82  0.24]
 [ 0.37  0.53]
 [ 0.93  0.01]
 [ 0.87  0.42]
 [ 0.94  0.5 ]]

Answer 5

这里有更多方法可以做同样的事情。在可读性方面，numpy.ndarray.flatten 更直接。

---

输入数组：

In [207]: arr1
Out[207]: 
array([[0.34, 0.26, 0.76],
       [0.79, 0.82, 0.37],
       [0.93, 0.87, 0.94]])

In [208]: arr2
Out[208]: 
array([[0.21, 0.73, 0.69],
       [0.35, 0.24, 0.53],
       [0.01, 0.42, 0.5 ]])

---

第一步，将它们展平：

In [209]: arr1_flattened = arr1.flatten()[:, np.newaxis]

In [210]: arr1_flattened
Out[210]: 
array([[0.34],
       [0.26],
       [0.76],
       [0.79],
       [0.82],
       [0.37],
       [0.93],
       [0.87],
       [0.94]])

In [211]: arr2_flattened = arr2.flatten()[:, np.newaxis]

In [212]: arr2_flattened
Out[212]: 
array([[0.21],
       [0.73],
       [0.69],
       [0.35],
       [0.24],
       [0.53],
       [0.01],
       [0.42],
       [0.5 ]])

然后将它们连接或堆叠：

# just horizontally stack (np.hstack) the flattened arrays
In [213]: np.hstack([arr1_flattened, arr2_flattened])
Out[213]: 
array([[0.34, 0.21],
       [0.26, 0.73],
       [0.76, 0.69],
       [0.79, 0.35],
       [0.82, 0.24],
       [0.37, 0.53],
       [0.93, 0.01],
       [0.87, 0.42],
       [0.94, 0.5 ]])

---

一行：

In [205]: np.hstack([arr1.flatten()[:, None], arr2.flatten()[:, None]])
Out[205]: 
array([[0.34, 0.21],
       [0.26, 0.73],
       [0.76, 0.69],
       [0.79, 0.35],
       [0.82, 0.24],
       [0.37, 0.53],
       [0.93, 0.01],
       [0.87, 0.42],
       [0.94, 0.5 ]])

# same thing can be done using np.concatenate
In [206]: np.concatenate([arr1.flatten()[:, None], arr2.flatten()[:, None]], axis=1)
Out[206]: 
array([[0.34, 0.21],
       [0.26, 0.73],
       [0.76, 0.69],
       [0.79, 0.35],
       [0.82, 0.24],
       [0.37, 0.53],
       [0.93, 0.01],
       [0.87, 0.42],
       [0.94, 0.5 ]])

---

请注意，所有堆叠方法（stack、hstack、vstack、dstack、column_stack）在后台调用 numpy.concatenate()。